Discrete-time Stochastic Extremum Seeking

We present discrete-time stochastic extremum seeking algorithms and prove their convergence using stochastic averaging theory that we recently developed. First, we provide a discrete stochastic extremum seeking algorithm for a static map, in which measurement noise is considered and an ergodic discrete-time stochastic process is used as the excitation signal. Second, for discrete-time nonlinear dynamical systems, in which the output equilibrium map has an extremum, we present a discretetime stochastic extremum seeking scheme and, with a singular perturbation reduction, we prove the stability of the reduced system. Compared with classical stochastic approximation methods, while the convergence that we prove is in a weaker sense, the conditions of the algorithm are easy to verify and no requirements (e.g., boundedness) are imposed on the algorithm itself.